Beyond Operational Efficiency: Understanding How AI-Enabled Service Quality and Trust in Automation Shape Passenger Satisfaction in Airports

Abstract:

In modern times, the airport industry faces rapid changes caused by the implementation of Artificial Intelligence (AI) solutions such as biometrics, automated border control, advanced luggage processing, and AI-powered customer service. While current literature pays much attention to the operational benefits of these technologies, little attention is paid to how customers perceive AI-powered services and how these perceptions lead to higher passenger satisfaction. This paper aims to offer a conceptual model explaining the relationships between AI service quality, trust in automation, and passenger satisfaction in airports. Based on the E-S-QUAL framework and Trust in Automation theory, the research proposes AI service quality as a higher-order construct comprising four dimensions: efficiency, system availability, fulfilment, and privacy. Each dimension is proposed to affect passenger satisfaction through direct and indirect pathways, with trust in automation serving as a mechanism that influences passengers’ confidence in intelligent systems and reduces uncertainty in technology-mediated experiences. Heathrow Airport is used only as an illustrative example of broader airport digitalisation trends, rather than as an empirical case study. The proposed framework extends E-S-QUAL to AI-based airport services and integrates service quality and trust into a unified conceptual model.